在Python中使用nets.resnet_utils构建自动驾驶神经网络

在Python中使用nets.resnet_utils构建自动驾驶神经网络，可以通过下面的示例来演示。

首先，我们需要导入所需的库和模块：

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, Activation, MaxPooling2D, AveragePooling2D, Flatten, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.initializers import glorot_uniform
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorical
from nets.resnet_utils import conv_block, identity_block

接下来，我们定义一个ResNet模型的类。在这个类中，我们定义了ResNet的架构和前向传播的计算流程。

class ResNetModel(Model):
    def __init__(self, input_shape=(64, 64, 3), classes=2):
        super(ResNetModel, self).__init__()

        self.conv1 = Conv2D(filters=64, kernel_size=(7, 7), strides=(2, 2), padding='same', kernel_initializer=glorot_uniform(seed=0))
        self.bn1 = BatchNormalization(axis=3)
        self.act1 = Activation('relu')
        self.pool1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))

        self.conv2_1 = conv_block(filters=[64, 64, 256], stage=2, block='a', stride=1)
        self.id2_2 = identity_block(filters=[64, 64, 256], stage=2, block='b')
        self.id2_3 = identity_block(filters=[64, 64, 256], stage=2, block='c')

        self.conv3_1 = conv_block(filters=[128, 128, 512], stage=3, block='a', stride=2)
        self.id3_2 = identity_block(filters=[128, 128, 512], stage=3, block='b')
        self.id3_3 = identity_block(filters=[128, 128, 512], stage=3, block='c')
        self.id3_4 = identity_block(filters=[128, 128, 512], stage=3, block='d')

        self.conv4_1 = conv_block(filters=[256, 256, 1024], stage=4, block='a', stride=2)
        self.id4_2 = identity_block(filters=[256, 256, 1024], stage=4, block='b')
        self.id4_3 = identity_block(filters=[256, 256, 1024], stage=4, block='c')
        self.id4_4 = identity_block(filters=[256, 256, 1024], stage=4, block='d')
        self.id4_5 = identity_block(filters=[256, 256, 1024], stage=4, block='e')
        self.id4_6 = identity_block(filters=[256, 256, 1024], stage=4, block='f')

        self.conv5_1 = conv_block(filters=[512, 512, 2048], stage=5, block='a', stride=2)
        self.id5_2 = identity_block(filters=[512, 512, 2048], stage=5, block='b')
        self.id5_3 = identity_block(filters=[512, 512, 2048], stage=5, block='c')

        self.avg_pool = AveragePooling2D(pool_size=(2, 2))
        self.flatten = Flatten()
        self.fc = Dense(classes, activation='softmax', kernel_initializer=glorot_uniform(seed=0))

    def call(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.act1(x)
        x = self.pool1(x)

        x = self.conv2_1(x)
        x = self.id2_2(x)
        x = self.id2_3(x)

        x = self.conv3_1(x)
        x = self.id3_2(x)
        x = self.id3_3(x)
        x = self.id3_4(x)

        x = self.conv4_1(x)
        x = self.id4_2(x)
        x = self.id4_3(x)
        x = self.id4_4(x)
        x = self.id4_5(x)
        x = self.id4_6(x)

        x = self.conv5_1(x)
        x = self.id5_2(x)
        x = self.id5_3(x)

        x = self.avg_pool(x)
        x = self.flatten(x)

        output = self.fc(x)

        return output

然后，我们定义一些训练和评估函数。

def train(X_train, Y_train, X_val, Y_val, num_epochs=10, batch_size=64, learning_rate=0.001):
    num_batches = int(X_train.shape[0] / batch_size)

    model = ResNetModel(input_shape=X_train.shape[1:], classes=Y_train.shape[1])
    optimizer = Adam(learning_rate=learning_rate)
    loss_fn = tf.keras.losses.CategoricalCrossentropy()

    train_loss = tf.keras.metrics.Mean()
    train_accuracy = tf.keras.metrics.CategoricalAccuracy()
    val_loss = tf.keras.metrics.Mean()
    val_accuracy = tf.keras.metrics.CategoricalAccuracy()

    for epoch in range(num_epochs):
        print("Epoch {}/{}".format(epoch+1, num_epochs))

        train_loss.reset_states()
        train_accuracy.reset_states()
        val_loss.reset_states()
        val_accuracy.reset_states()

        for batch in range(num_batches):
            x_batch = X_train[batch*batch_size:(batch+1)*batch_size,:,:,:]
            y_batch = Y_train[batch*batch_size:(batch+1)*batch_size,:]

            with tf.GradientTape() as tape:
                logits = model(x_batch)
                loss_value = loss_fn(y_batch, logits)

            grads = tape.gradient(loss_value, model.trainable_weights)
            optimizer.apply_gradients(zip(grads, model.trainable_weights))

            train_loss(loss_value)
            train_accuracy(y_batch, logits)

        for batch in range(int(X_val.shape[0] / batch_size)):
            x_val_batch = X_val[batch*batch_size:(batch+1)*batch_size,:,:,:]
            y_val_batch = Y_val[batch*batch_size:(batch+1)*batch_size,:]

            val_logits = model(x_val_batch)
            val_loss_value = loss_fn(y_val_batch, val_logits)

            val_loss(val_loss_value)
            val_accuracy(y_val_batch, val_logits)

        print("Train Loss: {:.4f}, Train Accuracy: {:.4f}, Val Loss: {:.4f}, Val Accuracy: {:.4f}".format(
            train_loss.result(), train_accuracy.result(), val_loss.result(), val_accuracy.result()))

def evaluate(X_test, Y_test, batch_size=64):
    num_batches = int(X_test.shape[0] / batch_size)

    model = ResNetModel(input_shape=X_test.shape[1:], classes=Y_test.shape[1])
    loss_fn = tf.keras.losses.CategoricalCrossentropy()
    test_loss = tf.keras.metrics.Mean()
    test_accuracy = tf.keras.metrics.CategoricalAccuracy()

    for batch in range(num_batches):
        x_batch = X_test[batch*batch_size:(batch+1)*batch_size,:,:,:]
        y_batch = Y_test[batch*batch_size:(batch+1)*batch_size,:]

        test_logits = model(x_batch)
        test_loss_value = loss_fn(y_batch, test_logits)

        test_loss(test_loss_value)
        test_accuracy(y_batch, test_logits)

    print("Test Loss: {:.4f}, Test Accuracy: {:.4f}".format(test_loss.result(), test_accuracy.result()))

最后，在主函数中调用训练和评估函数。

def main():
    # 加载数据、预处理数据等操作
    ...

    # 调用训练函数进行训练
    train(X_train, Y_train, X_val, Y_val, num_epochs=10, batch_size=64, learning_rate=0.001)

    # 调用评估函数进行测试评估
    evaluate(X_test, Y_test, batch_size=64)

if __name__ == '__main__':
    main()

以上是使用nets.resnet_utils构建自动驾驶神经网络的一个示例。您可以根据自己的数据和需求进行相应的修改和调整。